def gen_rec(self):
dt = self.dt
n = self.n
slope = self.slope
length = self.length
ks = self.ks
self.Steady_SQ()
for k in range(0, len(self.ks)):
ie1 = 10.0
aa = aw.trans_ana(ie1 * self.ks[k], ie1, length, 201, dt, n, slope)
aa.run()
# Normalize outflow and storage curve
out_s = aa.out_s
out_q = aa.out_q
out_s[:, 0] = out_s[:, 0] / aa.max_t
out_s[:, 1] = (out_s[:, 1] - aa.min_s) / (aa.max_s - aa.min_s)
out_q[:, 0] = out_q[:, 0] / aa.max_t
out_q[:, 1] = (out_q[:, 1] - aa.min_q) / (aa.max_q - aa.min_q)
# Keep outflow-storage curve
curve = [zip(out_q[:, 1], out_s[:, 1])][0]
self.header.append(ks[k])
self.curve.append(curve)
REC = {'header': self.header, 'curve': self.curve}
self.REC = REC
if self.rec_name:
pickle.dump(
REC,
open(self.folder + '/' + self.rec_name + '_CurveRec.flow',
'w'))
def compareSWRCurve():
ie0 = 100.0
ie1 = 5.0
ie2 = [5.5, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0,
100.0, 110.0, 130.0, 150.0, 170.0, 200.0]
L = 100.0
slope = 0.01
n = 0.1
dt = 0.5
x_space = 201
a = SWRCurves(L, x_space, dt, n, slope, 2, 4, useFile='ks1_SWRrec.pick')
a.fitPT()
# Run curves
TimeOfChange = 405.0
SWR_curves = list()
gen_Curves = list()
Qratios = list()
for i in range(0, len(ie2)):
ca = two_step.two_step_overland(ie0, ie1, ie2[i], L, x_space, dt, n,
slope)
ca.ie1_duration(TimeOfChange)
cb = ana_new.trans_ana(ie1, ie2[i], L, x_space, dt, n, slope)
ca.run()
cb.run()
SWR = bcdPoints(ca)
SWR[:, 0] = (SWR[:, 0] - SWR[0, 0])/(SWR[-1, 0] - SWR[0, 0])
SWR[:, 1] = SWR[:, 1]/SWR[0, 1]
SWR[:, 2] = SWR[:, 2]/SWR[0, 2]
SWR_curves.append(SWR)
Qratio = SWR[-1, 1]/SWR[0, 1]
curve, T = a.interpCurve(Qratio)
gen_Curves.append(curve)
Qratios.append(Qratio)
plt.figure(figsize=(10, 8))
ax = plt.subplot(111)
for k in range(0, len(ie2)):
color = randColor()
ax.plot(SWR_curves[k][:, 1], SWR_curves[k][:, 2], lw=1.0, ls='--',
label='{:4.3f}'.format(Qratios[k]), color=color)
ax.plot(gen_Curves[k][:, 0], gen_Curves[k][:, 1], lw=2.0, ls='-',
label='{:4.3f}'.format(gen_Curves[k][-1, 0]),
color=color)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1.0), ncol=2)
ax.set_xlabel('q')
ax.set_ylabel('s')
plt.title('Secondary Wetting Curve - Original and Generated')
plt.savefig('SWR_gen_compare.png')
plt.close()
def run():
plt.figure
_file = "/home/room801/anaoverland/Steady_fit/ks1_Rise_CurveRec.flow"
RiseRec = pickle.load(open(_file, 'r'))
_file = "/home/room801/anaoverland/Steady_fit/ks1_Fall_CurveRec.flow"
FallRec = pickle.load(open(_file, 'r'))
ks = [
1.01, 1.6, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 20.0, 40.0,
50.0, 60.0, 70.0, 80.0, 90.0, 100.0
]
for k in ks:
keystring = 'k=' + str(k)
out_q = RiseRec['Q'][keystring]
out_s = RiseRec['S'][keystring]
# Normalized curve to [0, 1]
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
plt.plot(out_q[:, 1],
out_s[0:len(out_q), 1],
color='dodgerblue',
linestyle='-')
for k in ks:
keystring = 'k=' + str(k)
out_q = FallRec['Q'][keystring]
out_s = FallRec['S'][keystring]
# Normalization to [0, 1]
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
plt.plot(out_q[:, 1],
out_s[0:len(out_q), 1],
color='tomato',
linestyle='-')
aa = an.trans_ana(10.0, 10.0 * 200, 100.0, 201, 0.5, 0.1, 0.01)
aa.run()
out_s = aa.out_s
out_q = aa.out_q
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
plt.plot(out_q[:, 1] / max(out_q[:, 1]),
out_s[0:len(out_q), 1] / max(out_s[:, 1]),
color='dodgerblue',
linestyle='-',
label='Rising Limb')
ab = an.trans_ana(10.0 * 200, 10.0, 100.0, 201, 0.5, 0.1, 0.01)
ab.run()
out_s = ab.out_s
out_q = ab.out_q
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
plt.plot(out_q[:, 1] / max(out_q[:, 1]),
out_s[0:len(out_q), 1] / max(out_s[:, 1]),
color='tomato',
linestyle='-',
label='Falling Limb')
# Rising Limb limit
ac = an.progression(200.0, 100.0, 201, 0.5, 0.1, 0.01)
ac.run()
out_q = ac.out_q
out_s = ac.out_s
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
plt.plot(out_q[:, 1], out_s[0:len(out_q), 1], 'k-', lw=1.5)
# Falling Limb limit
ad = an.Recession(200.0, 100.0, 201, 0.5, 0.1, 0.01)
ad.run()
out_q = ad.out_q
out_s = ad.out_s
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
plt.plot(out_q[:, 1],
out_s[0:len(out_q), 1],
linestyle='--',
color='dimgray',
lw=1.5)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.legend(loc=2)
plt.xlabel('$q^{{}*{}}$')
plt.ylabel('$s^{{}*{}}$')
plt.show()
def run():
aa = pickle.load(open('Steady_fit/fit_1_DownToUp.pick', 'r'))
t, out = aa.out_curve(4.3)
ab = ana.trans_ana(10.0, 43.0, 100.0, 1001, 1.0, 0.1, 0.01)
ab.run2()
out = out*ab.max_q
t = t*ab.max_t
plt.figure
plt.plot(t, out, '-b', label='Interpolation')
ab.plot_QT(plt_label='Analytical', LT='--r')
plt.xlabel('time (s)')
plt.ylabel('flowrate ($m^3/s$)')
plt.title('10 mm/hr to 43 mm/hr, k=4.3')
plt.legend(loc=4)
plt.savefig('10to43.png')
plt.close()
ac = pickle.load(open('Steady_fit/fit_1_UpToDown.pick', 'r'))
t, out = ac.out_curve(0.34)
ad = ana.trans_ana(100.0, 34.0, 100.0, 1001, 1.0, 0.1, 0.01)
ad.run2()
out = out*ad.min_q
t = t*ad.max_t
plt.figure
plt.plot(t, out, '-b', label='Interpolation')
ad.plot_QT(plt_label='Analytical', LT='--r')
plt.xlabel('time (s)')
plt.ylabel('flowrate ($m^3/s$)')
plt.title('100 mm/hr to 34 mm/hr, k=0.34')
plt.legend(loc=4)
plt.savefig('100to34.png')
plt.close()
plt.figure
aa.plot_C(0)
plt.title('$C_1$')
plt.legend(loc=3)
plt.savefig('C1.png')
plt.close()
plt.figure
aa.plot_C(1)
plt.title('$C_2$')
plt.legend(loc=4)
plt.savefig('C2.png')
plt.close()
plt.figure
aa.plot_C(2)
plt.title('$C_3$')
plt.legend(loc=1)
plt.savefig('C3.png')
plt.close()
plt.figure
aa.plot_C(3)
plt.title('$C_4$')
plt.legend(loc=1)
plt.savefig('C4.png')
plt.close()
plt.figure
aa.plot_C(4)
plt.title('$C_5$')
plt.legend(loc=1)
plt.savefig('C5.png')
plt.close()
def calc_testk(rain_rec, length):
dt = 5.0
Fall = sh.FallingLimb()
Rise = sh.RisingLimb()
std = pickle.load(open('Steady_fit/SQ_Steady.pick', 'r'))
rain_rec = np.array(rain_rec)
max_t = max(rain_rec[:, 0])
q_curve = list()
s_curve = list()
q2_curve = list()
s2_curve = list()
S0 = std.ie_to_storage(rainParse2(rain_rec, 0.0))
Q0 = std.ie_to_outflow(rainParse2(rain_rec, 0.0))
max_S = S0
max_Q = Q0
q_curve.append([0.0, Q0])
s_curve.append([0.0, S0])
q2_curve.append([0.0, Q0])
s2_curve.append([0.0, S0])
t = 0.0
S = S0
Q = Q0
S2 = S0
Q2 = Q0
k_rec = list()
k = rainParse(rain_rec, dt)/rainParse(rain_rec, 0.0) # Initial k setting
fixed_k = k
while t < max_t:
t = t + dt
c_ie = rainParse2(rain_rec, t)
pre_ie = rainParse2(rain_rec, t-dt)
if c_ie != pre_ie:
print(c_ie)
max_S = std.ie_to_storage(c_ie)
max_Q = std.ie_to_outflow(c_ie)
print(t, Q)
if t % 60.0 == 0 or c_ie != pre_ie:
if S < std.ie_to_storage(c_ie):
k, t_star = Rise.SQtok(
S/max_S, Q/max_Q, (std.ie_to_storage(c_ie)/max_S)**(5.0/3))
# k, t_star = Rise.SQtok(S/max_S, Q/max_Q, 5.0)
elif S > std.ie_to_storage(c_ie):
k, t_star = Fall.SQtok(S/max_S, Q/max_Q,
(std.ie_to_storage(c_ie)/max_S)**(5.0/3)
- 0.01)
print(k)
k_rec.append(k)
dS1 = h1(length, c_ie, k, S/max_S, dt, std, Fall, Rise)
dS2 = h2(length, c_ie, k, S/max_S, dS1, dt, std, Fall, Rise)
dS3 = h3(length, c_ie, k, S/max_S, dS2, dt, std, Fall, Rise)
dS = 0.25*dS1 + 0.75*dS3
dS1_2 = h1(length, c_ie, fixed_k, S2/max_S, dt, std, Fall, Rise)
dS2_2 = h2(length, c_ie, fixed_k, S2/max_S, dS1_2, dt, std, Fall, Rise)
dS3_2 = h3(length, c_ie, fixed_k, S2/max_S, dS2_2, dt, std, Fall, Rise)
dS_2 = 0.25*dS1_2 + 0.75*dS3_2
if k > 1.0:
Q = Rise.s_to_q(k, (S + dS)/max_S)*max_Q
elif k < 1.0:
Q = Fall.s_to_q(k, (S + dS)/max_S)*max_Q
if fixed_k > 1.0:
Q2 = Rise.s_to_q(fixed_k, (S2 + dS_2)/max_S)*max_Q
elif fixed_k < 1.0:
Q2 = Fall.s_to_q(fixed_k, (S2 + dS_2)/max_S)*max_Q
# print k, (S + dS)/max_S
S = S + length*c_ie*dt - Q*dt
S2 = S2 + length*c_ie*dt - Q2*dt
s_curve.append([t, S])
q_curve.append([t, Q])
s2_curve.append([t, S2])
q2_curve.append([t, Q2])
q_curve = np.array(q_curve)
s_curve = np.array(s_curve)
q2_curve = np.array(q2_curve)
s2_curve = np.array(s2_curve)
q_curve[:, 0] = q_curve[:, 0] - 60.0
s_curve[:, 0] = s_curve[:, 0] - 60.0
q2_curve[:, 0] = q2_curve[:, 0] - 60.0
s2_curve[:, 0] = s2_curve[:, 0] - 60.0
ab = an.trans_ana(54.3, 3.11, 100.0, 201, 1.0, 0.1, 0.01)
ab.run()
standard_k = np.ones(len(k_rec))
standard_k = standard_k*18.31
ie_list = np.arange(0.1, 160.1, 1.0)
std_s = std.ie_to_storage(ie_list/3600*10**-3)
std_o = std.ie_to_outflow(ie_list/3600*10**-3)
plt.figure
plt.plot(q_curve[:, 0], q_curve[:, 1], '-b', label='k-AutoSearch')
plt.plot(q2_curve[:, 0], q2_curve[:, 1], '-k', label='k-fixed')
ab.plot_QT(LT='--r', plt_label='Analytic')
plt.title('Outflow - Test k 54.3 mm/hr to 3.11 mm/hr')
plt.legend(loc=4)
plt.xlabel('time (s)')
plt.ylabel('Outflow ($m^3/s$)')
plt.xlim([0, t])
plt.savefig('modified-BKW_testk3.png')
plt.close()
plt.figure
plt.plot(s_curve[:, 0], s_curve[:, 1], '-b', label='k-AutoSearch')
plt.plot(s2_curve[:, 0], s2_curve[:, 1], '-k', label='k-fixed')
ab.plot_ST(LT='--r', plt_label='Analytic')
plt.legend(loc=4)
plt.title('Storage - Test k 54.3 mm/hr to 3.11 mm/hr')
plt.xlabel('time (s)')
plt.ylabel('Storage ($m^3$)')
plt.xlim([0, t])
plt.savefig('modified-BKW_storage_testk3.png')
plt.close()
plt.figure
plt.plot(std_o, std_s, '-r', linewidth=2.0)
plt.plot(q_curve[:, 1], s_curve[:, 1], '-b', label='k-AutoSearch')
plt.plot(q2_curve[:, 1], s2_curve[:, 1], '-m', label='k-fixed')
plt.legend(loc=2)
plt.title('S-Q - Test Case k 54.3 mm/hr to 3.11 m/hr')
plt.xlabel('Outflow ($m^3/s$)')
plt.ylabel('Storage ($m^3$)')
plt.savefig('storage-outflow_testk3.png')
plt.close()
def run(k):
if k > 1.0:
aa = an.trans_ana(10.0, 10.0*k, 100.0, 201, 0.5, 0.1, 0.01)
aa.run()
ab = an.trans_ana(30.0, 30.0*k, 100.0, 201, 0.5, 0.1, 0.01)
ab.run()
ac = an.trans_ana(60.0, 60.0*k, 100.0, 201, 0.5, 0.1, 0.01)
ac.run()
elif k < 1.0:
aa = an.trans_ana(50.0, 50.0*k, 100.0, 201, 0.5, 0.1, 0.01)
aa.run()
ab = an.trans_ana(100.0, 100.0*k, 100.0, 201, 0.5, 0.1, 0.01)
ab.run()
ac = an.trans_ana(200.0, 200.0*k, 100.0, 201, 0.5, 0.1, 0.01)
ac.run()
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))
axes[0].plot(ac.out_q[:, 0]/max(ac.out_q[:, 0]),
ac.out_q[:, 1]/max(ac.out_q[:, 1]), linestyle='-',
color='orange', lw=8.0,
label=str(ac.ie0*3600*1000)+' to '+str(ac.ie1*3600*1000))
axes[0].plot(ab.out_q[:, 0]/max(ab.out_q[:, 0]),
ab.out_q[:, 1]/max(ab.out_q[:, 1]), linestyle='-',
color='beige', lw=3.5,
label=str(ab.ie0*3600*1000)+' to '+str(ab.ie1*3600*1000))
axes[0].plot(aa.out_q[:, 0]/max(aa.out_q[:, 0]),
aa.out_q[:, 1]/max(aa.out_q[:, 1]), linestyle='--',
color='steelblue',
label=str(aa.ie0*3600*1000)+' to '+str(aa.ie1*3600*1000),
lw=2.0)
if k > 1:
axes[0].legend(loc=2)
elif k < 1:
axes[0].legend(loc=1)
axes[0].set_xlabel('$t_{{}*{}}$')
axes[0].set_ylabel('$q_{{}*{}}$')
axes[0].set_title('Outflow rate - Time')
axes[1].plot(ac.out_s[:, 0]/max(ac.out_s[:, 0]),
ac.out_s[:, 1]/max(ac.out_s[:, 1]), linestyle='-',
color='orange', lw=8.0,
label=str(ac.ie0*3600*1000)+' to '+str(ac.ie1*3600*1000))
axes[1].plot(ab.out_s[:, 0]/max(ab.out_s[:, 0]),
ab.out_s[:, 1]/max(ab.out_s[:, 1]), linestyle='-',
color='beige', lw=3.5,
label=str(ab.ie0*3600*1000)+' to '+str(ab.ie1*3600*1000))
axes[1].plot(aa.out_s[:, 0]/max(aa.out_s[:, 0]),
aa.out_s[:, 1]/max(aa.out_s[:, 1]), linestyle='--',
color='steelblue',
label=str(aa.ie0*3600*1000)+' to '+str(aa.ie1*3600*1000),
lw=2.0)
if k > 1:
axes[1].legend(loc=2)
elif k < 1:
axes[1].legend(loc=1)
axes[1].set_xlabel('$t_{{}*{}}$')
axes[1].set_ylabel('$S_{{}*{}}$')
axes[1].set_title('Storage - Time')
fig.suptitle('k = '+str(k))
plt.savefig('graph/eq_k_'+str(k)+'.png')
plt.close()
def run_farPoint(L, dt, n, S, x_grds, ie0, ie1):
aa = ana_new.trans_ana(ie0, ie1, L, x_grds, dt, n, S)
aa.run()
farPoint(aa.out_s, aa.out_q)
